www.nature.com/scientificreports OPEN Reveals of candidate active ingredients in Justicia and its anti‑thrombotic action of mechanism based on network pharmacology approach and experimental validation Zongchao Hong1,5, Ting Zhang2,5, Ying Zhang1, Zhoutao Xie1, Yi Lu1, Yunfeng Yao1, Yanfang Yang1,3,4*, Hezhen Wu1,3,4* & Bo Liu1,3* Thrombotic diseases seriously threaten human life. Justicia, as a common Chinese medicine, is usually used for anti‑infammatory treatment, and further studies have found that it has an inhibitory efect on platelet aggregation. Therefore, it can be inferred that Justicia can be used as a therapeutic drug for thrombosis. This work aims to reveal the pharmacological mechanism of the anti‑thrombotic efect of Justicia through network pharmacology combined with wet experimental verifcation. During the analysis, 461 compound targets were predicted from various databases and 881 thrombus‑related targets were collected. Then, herb‑compound‑target network and protein–protein interaction network of disease and prediction targets were constructed and cluster analysis was applied to further explore the connection between the targets. In addition, Gene Ontology (GO) and pathway (KEGG) enrichment were used to further determine the association between target proteins and diseases. Finally, the expression of hub target proteins of the core component and the anti‑thrombotic efect of Justicia’s core compounds were verifed by experiments. In conclusion, the core bioactive components, especially justicidin D, can reduce thrombosis by regulating F2, MMP9, CXCL12, MET, RAC1, PDE5A, and ABCB1. The combination of network pharmacology and the experimental research strategies proposed in this paper provides a comprehensive method for systematically exploring the therapeutic mechanism of multi‑component medicine. Te advancement of medical technology and the efective control of fatal diseases have greatly increased the average life span of human beings. Trombotic diseases, including pulmonary embolism, venous embolism, arterial embolism, cerebral embolism, etc., have seriously threatened human life. Sadly, the global public aware- ness of thrombotic diseases, especially pulmonary embolism and deep vein thrombosis, is not high. What needs more vigilance is that thromboembolic conditions have been estimated to account for 1 in 4 deaths worldwide in 20101. As a threatening disease, thrombosis can be accompanied by cancer2, obesity, and infammatory bowel disease3. Clinical data of patients with coronavirus disease (COVID-19) in 2019 also reported high venous thromboembolism and difuse intravascular coagulation, and thrombotic complications even became a sign of severe COVID-194,5. Te etiology and pathogenesis of thrombotic diseases are very complex and have not yet been fully clarifed. However, recent studies have shown that the occurrence and development of thrombotic diseases are mainly 1Faculty of Pharmacy, Hubei University of Chinese Medicine, Wuhan 430065, China. 2Afliated Hospital of Hubei University of Chinese Medicine, Hubei Hospital of Traditional Chinese Medicine, Wuhan, China. 3Key Laboratory of Traditional Chinese Medicine Resources and Chemistry of Hubei Province, Wuhan, China. 4Collaborative Innovation Center of Traditional Chinese Medicine of New Products for Geriatrics Hubei Province, Wuhan, China. 5These authors contributed equally: Zongchao Hong and Ting Zhang. *email: [email protected]; [email protected]; [email protected] Scientifc Reports | (2021) 11:17187 | https://doi.org/10.1038/s41598-021-96683-z 1 Vol.:(0123456789) www.nature.com/scientificreports/ related to fve reasons: (1) Vascular endothelial damage 6; (2) Increased platelet count 7; (3) Increased blood coagulation8; (4) Decreased anti-coagulant activity9; (5) Decreased fbrinolytic activity10. At present, the treat- ments for thrombotic diseases mainly includes anti-coagulation therapy, anti-platelet drug therapy, thrombolytic therapy, interventional therapy, and surgery 11. However, currently commercially available anti-thrombotic drugs do not seem to be fully applicable to all patients. Terefore, the development and use of new anti-thrombotic drugs is inevitable. Natural products, as a treasury for the development and discovery of new medicines, are worthy of our in- depth exploration. Te discovery of anti-thrombotic active ingredients from natural products can efectively enrich the drug bank for the treatment of thrombosis. Justicia (Justicia procumbens (L.) Ness), a resource-rich botanical, has been found to have a variety of pharmacological activities including anti-infammatory, liver protection, anti-oxidation, and anemia treatment12–14. Even more surprising is that we accidentally discovered its anti-platelet aggregation ability 15. A further conjecture is that the Justicia has a satisfactory anti-thrombotic efect or can assist the treatment of thrombotic diseases. However, the research evidence on the anti-thrombotic efect of Justicia is scarce, so that we do not know its anti-thrombotic mechanism. Te network pharmacology proposed by Hopkins University in 2008, is a big data integration method based on a large number of database resources and statistical algorithms. It is used to explore the synergy of multiple components, multiple targets, and multiple mechanisms for the treatment of diseases. From this point of view, the research strategy of network pharmacology is actually consistent with the theory of Chinese medicine. Net- work pharmacology is a new feld based on the development of "ingredient-target-disease" interaction network multidisciplinary integration theory, which provides a new way for modern medicine to explain the responsibility system of Chinese medicine, which also provides a novel strategy for our research purposes 16. Traditional Chi- nese Medicine (TCM) treats individuals or patients as systems in diferent states and has accumulated numerous herbal prescriptions. Te overall concept of TCM has a lot in common with the core ideas of emerging network pharmacology and network biology. Based on the existing network pharmacology technology, it is necessary to establish a new TCM network pharmacology method to predict the target spectrum of herbal compounds and pharmacological efects, reveal the drug-gene-disease co-module association, screen synergistic multiple compounds from herbal formulas in a high-throughput manner, and explain the combination rules and network regulation mode of action. Furthermore, the combination of computing, experimentation, and clinical practice is a promising strategy and direction for the future research of TCM network pharmacology17,18. In this work, we frst used network pharmacology to analyze the active components and signaling pathways of the anti-thrombotic efect of Justicia, and then verifed the results using component analysis, in vivo and in vitro experiments, and GeneChip Human Gene Array. Materials and methods Chemical database collection. All components of Justicia were obtained from A high-throughput experiment- and reference-guided database of traditional Chinese medicine (HERB, http:// herb. ac. cn) and Te PubChem Project (https:// pubch em. ncbi. nlm. nih. gov)19,20. HERB, a high-throughput experiment and reference guide database for traditional Chinese medicine, associates 12,933 targets and 28,212 diseases with 7263 herbs and 49,258 components, and provides six paired relationships in HERB, which will greatly support the moderni- zation of traditional Chinese medicine and guide reasonable modern drug discovery. Compounds target fshing. As described by Hsin-Yi Lin et al.21, we obtained the SMILES format of Jus- ticia’s ingredients from the PubChem database and fshed out the potential targets of compounds from HitPick (http:// mips. helmh oltz- muenc hen. de/ hitpi ck/), similarity ensemble approach (SEA, http:// sea. bkslab. org/), Tar- get Hunter of Small Molecule (https:// www. cblig and. org/ Targe tHunt er/) database. Ten, we integrated the tar- gets that were caught and removed duplicates (ensure the uniqueness of each target). Targets of thrombus. Te thrombus targets were gathered from the GeneCards database (https:// www. genec ards. org/)22, which provides information about disease targets. Te keywords “thrombus” were used, and a total of 881 targets were gathered. Obtaining protein–protein interaction data. Te targets to be analyzed were imported into the STRING database (https:// string- db. org/) to obtain the protein–protein interaction (PPI) relationship, and the obtained PPI data was exported for network visualization. Network construction and analysis. All networks were constructed in Cytoscape 3.8.1 sofware (https:// cytos cape. org/ downl oad. html), and the visualization network is composed of nodes and edges. Nodes represent components, targets, and herbs. To study further into the network, MCODE and Cytohubba plugin were introduced to generate clusters and identify hub nodes. Te MCODE plugin can determine the main center of the network and generate clusters of interconnected sub-clusters by setting K-cores. Increasing the value of K-core will generate fewer clusters and exclude smaller clusters. Moreover, we can fnd the node with the highest score, called SEEDs, which may have the opportunity to become the key target of this cluster. Hub genes are usually representative genes. Cytohubba is a plugin used by Cytoscape sofware to identify hub nodes. Te larger the score of the analysis result, the more critical the node and the darker the color displayed. Scientifc